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1. Characterizing zebra crossing zones using LiDAR data

Author

Esmorís Pena, Alberto Manuel, López Vilariño, David, Arango, David F., Varela García, Francisco Alberto, Cabaleiro Domínguez, José Carlos, Fernández Rivera, Francisco Manuel, Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías da Información, and Universidade de Santiago de Compostela. Departamento de Electrónica e Computación

Subjects
LiDAR, Computational Theory and Mathematics, Safety of pedestrians, Building and Construction, Light detection and ranging, Computer Graphics and Computer-Aided Design, Zebra crossing zones, Computer Science Applications, Civil and Structural Engineering
Abstract

Light detection and ranging (LiDAR) scanning in urban environments leads to accurate and dense three-dimensional point clouds where the different elements in the scene can be precisely characterized. In this paper, two LiDAR-based algorithms that complement each other are proposed. The first one is a novel profiling method robust to noise and obstacles. It accurately characterizes the curvature, the slope, the height of the sidewalks, obstacles, and defects such as potholes. It was effective for 48 of 49 detected zebra crossings, even in the presence of pedestrians or vehicles in the crossing zone. The second one is a detailed quantitative summary of the state of the zebra crossing. It contains information about the location, the geometry, and the road marking. Coarse grain statistics are more prone to obstacle-related errors and are only fully reliable for 18 zebra crossings free from significant obstacles. However, all the anomalous statistics can be analyzed by looking at the associated profiles. The results can help in the maintenance of urban roads. More specifically, they can be used to improve the quality and safety of pedestrian routes Consellería de Cultura, Educación e Ordenación Universitaria, Grant/Award Numbers: accreditation 2019-2022 ED431G-2019/04, 2022-2024, ED431C2022/16, ED481A-2020/231; European Regional Development Fund (ERDF); CiTIUS-Research Center in Intelligent Technologies of the University of Santiago de Compostela as a Research Center of the Galician University System; Ministry of Economy and Competitiveness, Government of Spain, Grant/Award Number: PID2019-104834GB-I00; National Department of Traffic (DGT) through the project Analysis of Indicators Big-Geodata on Urban Roads for the Dynamic Design of Safe School Roads, Grant/Award Number: SPIP2017-02340 SI

Published
2023
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2. Automatic Generation of Urban Road 3D Models for Pedestrian Studies from LiDAR Data.

Author

Fernández-Arango, David, Varela-García, Francisco-Alberto, González-Aguilera, Diego, and Lagüela-López, Susana

Subjects
*LIDAR, *PUBLIC spaces, *POINT cloud, *CITIES & towns, *PEDESTRIANS, *SURFACE impedance, *ROAD closures
Abstract

The point clouds acquired with a mobile LiDAR scanner (MLS) have high density and accuracy, which allows one to identify different elements of the road in them, as can be found in many scientific references, especially in the last decade. This study presents a methodology to characterize the urban space available for walking, by segmenting point clouds from data acquired with MLS and automatically generating impedance surfaces to be used in pedestrian accessibility studies. Common problems in the automatic segmentation of the LiDAR point cloud were corrected, achieving a very accurate segmentation of the points belonging to the ground. In addition, problems caused by occlusions caused mainly by parked vehicles and that prevent the availability of LiDAR points in spaces normally intended for pedestrian circulation, such as sidewalks, were solved in the proposed methodology. The innovation of this method lies, therefore, in the high definition of the generated 3D model of the pedestrian space to model pedestrian mobility, which allowed us to apply it in the search for shorter and safer pedestrian paths between the homes and schools of students in urban areas within the Big-Geomove project. Both the developed algorithms and the LiDAR data used are freely licensed for their use in further research. [ABSTRACT FROM AUTHOR]

Published
2022
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